This report reviews previous work on Viterbi algorithm and decoder design. Our work is based on a (2, 1, 4) architecture with hard decision. Different hardware architectures are applied to Add-Compare-Select unit (ACS) and the entire system, and the performances on energy, area, and speed are compared. Keywords-convolutional code; maximum likelihood decoding; Viterbi algorithm

The Viterbi algorithm is the conventional decoding algorithm most widely adopted for sequence labeling. Viterbi decoding is, however, prohibitively slow when the label set is large, because its time complexity is quadratic in the number of labels. This paper proposes an exact decoding algorithm that overcomes this problem. A novel property of our algorithm is that it efficiently reduces the lab...

Maximum likelihood decoding of convolutional codes is finding considerable application in communication systems and several decoders of this type have recently been built. These decoders implement an evolved form of a decoding procedure that was originally described by Viterbi in 1967. In this document we describe the problems of reducing the Viterbi algorithm to hardware, the various tradeoffs...

Hidden Markov models (HMMs) are routinely used for analysis of long genomic sequences to identify various features such as genes, CpG islands, and conserved elements. A commonly used Viterbi algorithm requires O(mn) memory to annotate a sequence of length n with an m-state HMM, which is impractical for analyzing whole chromosomes. In this paper, we introduce the on-line Viterbi algorithm for de...

Convolutional codes are non blocking codes that can be designed to either error detecting or correcting. Convolution coding has been used in communication systems including deep space communication and wireless communication. At the receiver end the original message sequence is obtained from the received data using Viterbi decoder. It implements Viterbi Algorithm which is a maximum likelihood a...

This paper presents an iterative CKY parsing algorithm for probabilistic contextfree grammars (PCFG). This algorithm enables us to prune unnecessary edges produced during parsing, which results in more efficient parsing. Since pruning is done by using the edge’s inside Viterbi probability and the upper-bound of the outside Viterbi probability, this algorithm guarantees to output the exact Viter...

In this paper we compare the performance of acoustic HMMs obtained through Viterbi training with that of acoustic HMMs obtained through the Baum-Welch algorithm. We present recognition results for discrete and continuous HMMs, for read and spontaneous speech databases, acquired at 8 and 16 kHz. We also present results for a combination of Viterbi and Baum-Welch training, intended as a trade-off...